The 16 SQL queries against 422,729 rows indicate moderate data volume; query execution likely consumed 60–70% of total pipeline runtime, with the remainder split between LLM synthesis and I/O overhead. This workload is suitable for scheduled runs on a 4–6 hour cadence but risks timeout on on-demand execution if latency-sensitive consumers are present. The primary cost lever is query optimization—specifically, pre-aggregating stock-performance metrics and AI-surge indicators upstream rather than computing them row-by-row during synthesis, which would reduce both SQL execution time and the volume of rows passed to LLM stages.